class_docstr: Defines an evaluator for the COCO dataset.
  
  This evaluator is usually used through a metric returned by
  :func:`make_coco_metrics`.
  
  This mostly acts a wrapper around :class:`COCOEval` from the `pycocotools`
  library.
code: def prepare_for_coco_detection(self, predictions):
      coco_results = []
      for original_id, prediction in predictions.items():
          if len(prediction) == 0:
              continue
  
          boxes = prediction["boxes"]
          boxes = convert_to_xywh(boxes).tolist()
          scores = prediction["scores"].tolist()
          labels = prediction["labels"].tolist()
  
          coco_results.extend(
              [
                  {
                      "image_id": original_id,
                      "category_id": labels[k],
                      "bbox": box,
                      "score": scores[k],
                  }
                  for k, box in enumerate(boxes)
              ]
          )
      return coco_results
  
imports: import copy
  import io
  from collections import OrderedDict
  from contextlib import redirect_stdout
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pycocotools.mask
  import torch
  import torch.distributed
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from avalanche.evaluation.metrics.detection import DetectionEvaluator, TCommonDetectionOutput
label_desc: Use image node_id 7 for calling the CocoEvaluator obj's underlying member method code with example usage: obj.prepare_for_coco_detection(predictions) and returns: coco_results